用特征空间锚点生成合成数据，实现小样本生理信号增量学习
AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion
- 通过特征空间锚点引导生成合成样本，替代存储原始数据
- 在三个生理时序数据集上有效防止知识遗忘，提升新类别适应能力
- 适合数据稀缺且需持续学习的医疗信号场景
深度学习模型在诸多实际应用中表现卓越，其成功通常依赖于具备强泛化能力的基模型，能在少量支持数据下适应新任务并保持已有知识。然而，这些成果依赖大量高质量数据，而生物医学领域常面临数据有限且以增量方式出现的问题。此时模型需在保留旧知识的同时适应新类别。少样本类增量学习（FSCIL）为此提供可行方案，但同样受限于对强基模型的依赖。为克服此局限，本文提出AnchorInv，采用直接高效的缓存重放策略：不存储原始数据，而是基于特征空间中的锚点生成合成样本。该方法保护隐私并正则化模型以增强适应性。在三个公开生理时序数据集上的评估表明，AnchorInv能有效防止知识遗忘，显著提升对新类别的适应性能，优于现有最先进方法。
原文摘要 · Abstract (English)
Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quality data, which is often lacking in specialized biomedical applications. In such fields, models are usually developed with limited data that arrive incrementally with novel categories. This requires the model to adapt to new information while preserving existing knowledge. Few-Shot Class-Incremental Learning (FSCIL) methods offer a promising approach to addressing these challenges, but they also depend on strong base models that face the same aforementioned limitations. To overcome these constraints, we propose AnchorInv following the straightforward and efficient buffer-replay strategy. Instead of selecting and storing raw data, AnchorInv generates synthetic samples guided by anchor points in the feature space. This approach protects privacy and regularizes the model for adaptation. When evaluated on three public physiological time series datasets, AnchorInv exhibits efficient knowledge forgetting prevention and improved adaptation to novel classes, surpassing state-of-the-art baselines.
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